As discussed in [1]} reward design is one of the foremost challenges in real-world reinforcement learning. Specifying a quality reward is an important task because the reward design impacts the RL algorithm's ability to optimally select actions corresponding to the desired goal and the speed at which it learns [2]}. There are a variety of ways to modify and construct rewards. For example, one may design rewards to encourage RL algorithms to learn faster by encoding domain information, e.g., information about the relative effectiveness of different actions [3]}, [4]}, [5]}. Additionally, in practice, rewards have been penalized to account for the cost of taking certain actions [6]} and to satisfy domain-specific constraints [7]}.
